Microphone array signal enhancement

ABSTRACT

A system and method facilitating signal enhancement utilizing an adaptive filter is provided. The invention includes an adaptive filter that filters an input based upon a plurality of adaptive coefficients, the adaptive filter modifying at least one of the adaptive coefficients based on a feedback output. The invention further includes a feedback component that provides the feedback output based, at least in part, upon a non-linear function of the acoustic reverberation reduced output.  
     The invention further provides a noise statistics component that stores noise statistics associated with a noise portion of an input signal and a signal+noise statistics component that stores signal+noise statistics associated with a signal and noise portion of the input signal. The invention further provides a spatial filter that provides an output signal based, at least in part, upon a filtered input signal, the filtering being based, at least in part, upon a weighted error calculation of the noise statistics and the signal+noise statistics.

TECHNICAL FIELD

[0001] The present invention relates generally to acoustic signalenhancement, and more particularly to a system and method facilitatingsignal enhancement utilizing an adaptive filter.

BACKGROUND OF THE INVENTION

[0002] The quality of speech captured by personal computers can bedegraded by environmental noise and/or by reverberation (e.g., caused bythe sound waves reflecting off walls and other surfaces).Quasi-stationary noise produced by computer fans and air conditioningcan be significantly reduced by spectral subtraction or similartechniques. In contrast, removing non-stationary noise and/or reducingthe distortion caused by reverberation are much harder problems.De-reverberation is a difficult blind deconvolution problem due to thebroadband nature of speech and the high order of the equivalent impulseresponse from the speaker's mouth to the microphone. The problem is, ofcourse, alleviated by the use of microphone headsets, but those areusually inconvenient to the user.

[0003] Using signal processing to improve the quality of speech acquiredby microphone(s) has been a long-standing interest in the Digital SignalProcessing community, with some of the most promising technologies beingbased on microphone arrays. The microphone array literature isparticularly populated with algorithms based on the Generalized SidelobeCanceller (GSC), but performance degrades quickly with reverberation.Other algorithms are based on optimum filtering concepts, or signalsubspace projection. A different approach comes from Blind SourceSeparation (BSS). Curiously, while BSS techniques perform extremely wellin some environments, they tend to be overly sensitive to ambientconditions (e.g., room reverberation), and perform poorly in mostreal-world scenarios.

SUMMARY OF THE INVENTION

[0004] The following presents a simplified summary of the invention inorder to provide a basic understanding of some aspects of the invention.This summary is not an extensive overview of the invention. It is notintended to identify key/critical elements of the invention or todelineate the scope of the invention. Its sole purpose is to presentsome concepts of the invention in a simplified form as a prelude to themore detailed description that is presented later.

[0005] The present invention provides for a signal enhancement systemreducing reverberation and/or noise in an input signal. According to anaspect of the present invention, an audio enhancement system (e.g.,acoustic reverberation reduction) having an adaptive filter and afeedback component is provided. Optionally, the system can furtherinclude a linear prediction (LP) analyzer and/or a LP synthesis filter.

[0006] The system can enhance signal(s), for example, to improve thequality of speech that is acquired by a microphone by reducingreverberation. The system utilizes, at least in part, the principle thatcertain characteristics of reverberated speech are measurably differentfrom corresponding characteristics of clean speech. The system canemploy a filter technology (e.g., reverberation reducing) based on anon-linear function, for example, the kurtosis metric.

[0007] The adaptive filter filters an input signal based, at least inpart, upon a plurality of adaptive coefficients. The adaptive filtermodifies at least one of the plurality of adaptive coefficients based,at least in part, upon a feedback output of the feedback component. Theadaptive filter provides a quality enhanced (e.g., acousticreverberation reduced) output.

[0008] The adaptive filter employs a filter technology (e.g.,reverberation measuring) based on a non-linear function, for example,the kurtosis metric. The feedback component provides the feedback outputwhich is used by the adaptive filter to control filter updates. Thefeedback output can be based, at least in part, upon a non-linearfunction of the quality enhanced (e.g., acoustic reverberation reduced)output of the adaptive filter.

[0009] The LP analyzer analyzes the input signal and provides the LPresidual output. The LP analyzer can be a filter constrained to be anall-pole linear filter that performs a linear prediction of the nextsample as a weighted sum of past samples.

[0010] The LP synthesis filter filters the acoustic reverberation outputfrom the adaptive filter and provides a processed output signal. The LPsynthesis filter can perform the inverse function of the LP analyzer.

[0011] Another aspect of the present invention provides for an audioenhancement system (e.g., acoustic reverberation reduction) systemhaving a first adaptive filter, an LP analyzer, a second adaptive filterand a feedback component.

[0012] The first adaptive filter filters an input signal based, at leastin part, upon a plurality of adaptive coefficients. The first adaptivefilter provides a quality enhanced (e.g., acoustic reverberationreduced) output. This filter is adaptive, but in the sense that thecoefficients will vary with the signal. In fact, the coefficients ofthis first adaptive filter are copied (e.g., periodically) from thesecond adaptive filter. The second filter is the one that actuallydrives the adaptation process.

[0013] The LP analyzer analyzes the input signal and provides a linearprediction residual output. The LP analyzer can be a filter constrainedto be an all-pole linear filter that performs a linear prediction of thenext sample as a weighted sum of past samples.

[0014] The second adaptive filter filters the linear prediction outputreceived from the LP analyzer based, at least in part, upon theplurality of adaptive coefficients. The second adaptive filter isadapted to modify at least one of the plurality of adaptive coefficientsbased, at least in part, upon a feedback output from the feedbackcomponent.

[0015] The second adaptive filter employs a filter technology (e.g.,reverberation measuring) based on a non-linear function, for example,the kurtosis metric. The second adaptive filter further provides anoutput to the feedback component.

[0016] The feedback component provides the feedback output which is usedby the second adaptive filter to control filter updates. The feedbackoutput can be based, at least in part, upon a non-linear function of theoutput of the second adaptive filter.

[0017] An aspect of the present invention provides for the audioenhancement system system to be extended to a multi-channelimplementation.

[0018] Yet another aspect of the present invention provides for afrequency domain audio enhancement (e.g., reverberation reduction)system having a first adaptive filter, an LP analyzer, a second adaptivefilter and a feedback component.

[0019] The system uses a subband adaptive filtering structure based, forexample, on the modulated complex lapped transform (MCLT). Since thesubband signal has an approximately flat spectrum, faster convergenceand/or reduced sensitivity to noise, for example, can be achieved.

[0020] The first adaptive filter includes a frequency transform, aplurality of adaptive coefficients and an inverse frequency transform.The frequency transform performs a frequency domain transform of aninput signal. In one example, the frequency transform employs an MCLTthat decomposes the input signal into M complex subbands. However, it isto be appreciated that any suitable frequency domain transform can beemployed by the frequency transform in accordance with the presentinvention.

[0021] The plurality of adaptive coefficients are used by the firstadaptive filter to filter the input signal. The inverse frequencytransform performs an inverse frequency domain transform of the filteredfrequency transformed input signal. For example, the inverse frequencytransform can perform an inverse MCLT.

[0022] The LP analyzer analyzes the input signal and provides a linearprediction residual output. The LP analyzer can be a filter constrainedto be an all-pole linear filter that performs a linear prediction of thenext sample as a weighted sum of past samples.

[0023] The second adaptive filter includes a frequency transform, aplurality of adaptive coefficients and an inverse frequency transform.The frequency transform performs a frequency domain transform of thelinear prediction output. The second adaptive filter filters thefrequency domain transformed linear prediction output based, at least inpart, upon the plurality of adaptive coefficients. The second adaptivefilter modifies at least one of the plurality of adaptive coefficientsbased, at least in part, upon a feedback output from the feedbackcomponent. The second adaptive filter further provides an output.

[0024] The feedback component provides the feedback output based, atleast in part, upon a non-linear function of the output of the secondadaptive filter. Also provided is a multi-channel audio enhancement(e.g., acoustic reverberation reduction) system in accordance with anaspect of the present invention.

[0025] Another aspect of the present invention provides for a noisereduction system having a first statistics component, a secondstatistics component and a spatial filter. Optionally, the system caninclude a voice activity detector.

[0026] The first (e.g., noise) statistics component stores statisticsassociated with a first portion or type of an input signal (e.g., noisestatistics associated with a noise portion of an input signal). Thesecond (e.g., signal+noise) statistics component stores statisticsassociated with a second portion or type of an input signal (e.g.,signal+noise statistics associated with a signal and noise portion ofthe input signal). The spatial filter provides an output signal, theoutput signal being based, at least in part, upon a filtered inputsignal, the filtering being based, at least in part, upon a weightederror calculation of the first statistics (e.g., noise statistics) andthe second statistics (e.g., signal+noise statistics). The spatialfilter can be a fixed filter or an adaptive filter.

[0027] The voice activity detector provides information to the firststatistic component and/or the second statistics component based, atleast in part, upon the output signal of the spatial filter. The voiceactivity detector detects when substantially only noise is present(e.g., silent period(s)) and provides the input signal, for example, tothe first statistics component. When speech is present, possibly withnoise, the input signal can be provided to the second statisticscomponent by the voice activity detector.

[0028] The spatial filter can utilize an improved Wiener filter based,at least in part, upon a weighted error calculation of the firststatistics (e.g., noise) and the second statistics (e.g., signal+noise).

[0029] Yet another aspect of the present invention provides for a LeastMeans Squared (LMS)-based noise reduction system having a signal+noisebuffer, a noise buffer, a signal composer, a filter, an LMS filter anddifferential component.

[0030] A synthetic input signal and its associated desired signal aregenerated by the signal composer by adding data from the signal+noisebuffer and/or the noise buffer to the input data. This synthetic signalis used to adapt the LMS filter. The filter coefficients are copied(e.g., continuously) to the filter, which directly processes the inputsignal.

[0031] Another aspect of the present invention provides for a signalenhancement system having a frequency transform, a voice activitydetector, a noise buffer, a signal+noise buffer, a filter, a noiseadaptive filter, a reverberation adaptive filter, an inverse frequencytransform, a noise feedback component and a reverberation feedbackcomponent.

[0032] The voice activity detector provides information to the noisebuffer and/or the signal+noise buffer based, at least in part, upon theinput signal. The voice activity detector detects when substantiallyonly noise is present (e.g., silent period(s)) and provides the inputsignal to the noise buffer. When speech is present, possibly with noise,the input signal can be provided to the noise+signal buffer by the voiceactivity detector. In one example, the voice activity discards sample(s)of the input signal which it is unable to classify as noise or“signal+noise”.

[0033] The signals stored in the noise buffer are used to train thenoise adaptive filter while the signal stored in the noise+signal bufferare used train the reverberation adaptive filter.

[0034] The filter filters the frequency transform input signal receivedfrom the frequency transform based, at least in part, upon a pluralityof adaptive coefficients. The filter provides a filtered output to theinverse frequency transform that performs an inverse frequency transform(e.g., inverse MCLT) and provides an acoustic enhancement signal output.The plurality of adaptive coefficients utilized by the filter aremodified by the noise adaptive filter and/or the reverberation adaptivefilter.

[0035] The noise adaptive filter filters the signals stored in the noisebuffer based, at least in part, upon the plurality of adaptivecoefficients. The noise adaptive filter is adapted to modify at leastone of the plurality of adaptive coefficients based, at least in part,upon a feedback output from the noise feedback component.

[0036] The noise adaptive filter can employ the improved Wiener filtertechnique(s) described herein. The noise adaptive filter furtherprovides an output to the noise feedback component.

[0037] The noise feedback component provides the noise reductionfeedback output based, at least in part, upon a weighted errorcalculation of the output of the noise reduction adaptive filter.

[0038] The reverberation adaptive filter filters the signals stored inthe noise+signal buffer based, at least in part, upon the plurality ofadaptive coefficients. The reverberation adaptive filter is adapted tomodify at least one of the plurality of adaptive coefficients based, atleast in part, upon a feedback output from the reverberation feedbackcomponent.

[0039] The reverberation adaptive filter employs a reverberationmeasuring filter technology based on a non-linear function, for example,the kurtosis metric. The reverberation adaptive filter further providesan output to the reverberation feedback component.

[0040] The reverberation feedback component provides the feedback outputwhich is used by the reverberation adaptive filter to control filterupdates. The feedback output can be based, at least in part, upon anon-linear function of the output of the reverberation adaptive filter.

[0041] Other aspects of the present invention provide methods forreducing acoustic reverberation, reducing acoustic noise, and enhancingan acoustic signal. Further provided are a computer readable mediumhaving computer usable instructions for a system for facilitatingacoustic reverberation reduction and a computer readable medium havingcomputer usable instructions for a system for acoustic noise reduction.A data packet adapted to be transmitted between two or more computercomponents that facilitates acoustic reverberation reduction, the datapacket comprising a data field comprising a plurality of adaptivecoefficients, at least one of the plurality of adaptive coefficientshaving been modified based, at least in part, upon a feedback outputbased, at least in part, upon a non-linear function of an acousticreverberation reduced output is provided. Also provided is a data packetadapted to be transmitted between two or more computer components thatfacilitates acoustic noise reduction, the data packet comprising a datafield comprising a plurality of adaptive coefficients, at least one ofthe plurality of adaptive coefficients having been modified based, atleast in part, upon a feedback output based, at least in part, upon aweighted error calculation of noise statistics and signal+noisestatistics.

[0042] To the accomplishment of the foregoing and related ends, certainillustrative aspects of the invention are described herein in connectionwith the following description and the annexed drawings. These aspectsare indicative, however, of but a few of the various ways in which theprinciples of the invention may be employed and the present invention isintended to include all such aspects and their equivalents. Otheradvantages and novel features of the invention may become apparent fromthe following detailed description of the invention when considered inconjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0043]FIG. 1 is a block diagram of an audio enhancement system inaccordance with an aspect of the present invention.

[0044]FIG. 2 is a block diagram of an audio enhancement system inaccordance with an aspect of the present invention.

[0045]FIG. 3 is a block diagram of a frequency domain audio enhancementsystem in accordance with an aspect of the present invention.

[0046]FIG. 4 is a block diagram of a multi-channel audio enhancementsystem in accordance with an aspect of the present invention.

[0047]FIG. 5 is a block diagram of an acoustic noise reduction system inaccordance with an aspect of the present invention.

[0048]FIG. 6 is a block diagram of a frequency domain acoustic noisereduction system in accordance with an aspect of the present invention.

[0049]FIG. 7 is a block diagram of an acoustic noise reduction system inaccordance with an aspect of the present invention.

[0050]FIG. 8 is a block diagram of a signal enhancement system inaccordance with an aspect of the present invention.

[0051]FIG. 9 is a flow chart illustrating a methodology for reducingacoustic reverberation in accordance with an aspect of the presentinvention.

[0052]FIG. 10 is a flow chart illustrating a methodology for reducingacoustic reverberation in accordance with an aspect of the presentinvention.

[0053]FIG. 11 is a flow chart illustrating a methodology for reducingacoustic noise in accordance with an aspect of the present invention.

[0054]FIG. 12 is a flow chart illustrating a methodology for reducingacoustic noise in accordance with an aspect of the present invention.

[0055]FIG. 13 is a flow chart illustrating a methodology for enhancingan acoustic signal in accordance with an aspect of the presentinvention.

[0056]FIG. 14 is a flow chart further illustrating the method of FIG. 13in accordance with an aspect of the present invention.

[0057]FIG. 15 illustrates an example operating environment in which thepresent invention may function.

DETAILED DESCRIPTION OF THE INVENTION

[0058] The present invention is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the present invention. It may be evident,however, that the present invention may be practiced without thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form in order to facilitate describing thepresent invention.

[0059] As used in this application, the term “computer component” isintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution. For example, a computer component may be, but is not limitedto being, a process running on a processor, a processor, an object, anexecutable, a thread of execution, a program, and/or a computer. By wayof illustration, both an application running on a server and the servercan be a computer component. One or more computer components may residewithin a process and/or thread of execution and a component may belocalized on one computer and/or distributed between two or morecomputers.

[0060] Referring to FIG. 1, an audio enhancement system 100 inaccordance with an aspect of the present invention is illustrated. Thesystem 100 includes an adaptive filter 110 and a feedback component 120.Optionally, the system 100 can further include a linear predictionanalyzer 130 (LP prediction analyzer 130) and/or a linear predictionsynthesis filter 140 (LP synthesis filter 140).

[0061] The system 100 can enhance signal(s), for example to improve thequality of speech that is acquired by a microphone (not shown) byreducing reverberation. The system 100 utilizes, at least in part, theprinciple that certain characteristics of reverberated speech aredifferent from those of clean speech. The system 100 can employ a filtertechnology (e.g., reverberation measuring) based on a non-linearfunction, for example, the kurtosis metric. In one example, thenon-linear function is based on increasing a non-linear measure ofspeechness (e.g., based on maximizing kurtosis of an LPC residual of anoutput signal).

[0062] It has been observed that the kurtosis of the linear prediction(LP) analysis residual of speech reduces with reverberation. In oneexample, the system 100 utilizes a reverberation-reducing algorithmfacilitating an adaptive gradient-descent algorithm that maximizes LPresidual kurtosis. In other words, the system 100 facilitatesidentifying blind deconvolution filter(s) that make the LP residualsnon-Gaussian. The received noisy reverberated speech signal is x(n) andits corresponding LP residual output (e.g., received from the optionalLP analyzer 130) is {tilde over (x)}(n). The adaptive filter 110 (h(n))is an L-tap adaptive filter at time n. The output of the adaptive filter110 is {tilde over (y)}(n)=h^(T)(n){tilde over (x)}(n),Where {tilde over(x)}(n)=[{tilde over (x)}(n−L+1) . . . {tilde over (x)}(n−1){tilde over(x)}(n)]^(T). The optional LP synthesis filter 140 yields y(n), thefinal processed signal. Adaptation of the adaptive filter 110 (h(n)) issimilar to the traditional least means squared (LMS) adaptive filter,except that instead of a desired signal a feedback output from thefeedback component 120, f(n) is used.

[0063] The adaptive filter 110 filters the LP residual output based, atleast in part, upon a plurality of adaptive coefficients. The adaptivefilter 110 modifies at least one of the plurality of adaptivecoefficients based, at least in part, upon the feedback output of thefeedback component 120. The adaptive filter 110 provides a qualityenhanced (e.g., acoustic reverberation reduced) output.

[0064] The adaptive filter 110 employs a reverberation measuring filtertechnology based on a non-linear function, for example, the kurtosismetric. In the instance in which the adaptive filter 110 is adapted tomaximize the kurtosis of an input signal {tilde over (x)}(n), forexample, based, at least in part, upon the following equation:

J(n)=E{{tilde over (y)} ⁴(n)}/E ² {{tilde over (y)} ²(n)}−3  (1)

[0065] where the expectations E{ } can be estimated from sampleaverages. The gradient of J(n) with respect to the current filter is:$\begin{matrix}{\frac{\partial J}{\partial h} = \frac{4( {{E\{ {\overset{\sim}{y}}^{2} \} E\{ {{\overset{\sim}{y}}^{3}\overset{\sim}{x}} \}} - {E\{ {\overset{\sim}{y}}^{4} \} E\{ {\overset{\sim}{y}\quad \overset{\sim}{x}} \}}} )}{E^{3}\{ {\overset{\sim}{y}}^{2} \}}} & (2)\end{matrix}$

[0066] where the dependence on the time n is not written for simplicity.The gradient can be approximated by: $\begin{matrix}{{\frac{\partial J}{\partial h} \approx {( \frac{4( {( {{E\{ {\overset{\sim}{y}}^{2} \} {\overset{\sim}{y}}^{2}} - {E\{ {\overset{\sim}{y}}^{4} \}}} )\overset{\sim}{y}}\quad )}{E^{3}\{ {\overset{\sim}{y}}^{2} \}} )\overset{\sim}{\quad x}}} = {{f(n)}{\overset{\sim}{x}(n)}}} & (3)\end{matrix}$

[0067] where f(n) is the feedback output (e.g., function) received fromthe feedback component 120. For continuous adaptation, E{{tilde over(y)}²(n)} and E{{tilde over (y)}⁴(n)} are estimated recursively. Thefinal structure of the update equations for a filter that maximizes thekurtosis of the LP residual of the input waveform is then given by:

h(n+1)=h(n)+μf(n){tilde over (x)}(n)  (4)

[0068] where μ controls the speed of adaptation of the adaptive filter110.

[0069] In one example, the non-linear function is based, at least inpart, upon selecting a threshold and providing positive feedback forsamples above that threshold and negative feedback for samples belowthat threshold.

[0070] The feedback component 120 provides the feedback output f(n)which is used by the adaptive filter 110 to control filter updates. Thefeedback output can be based, at least in part, upon a non-linearfunction of the quality enhanced output (e.g., acoustic reverberationreduced) of the adaptive filter 110. For example, the feedback outputcan be based, at least in part, upon the following equations:$\begin{matrix}\begin{matrix}\begin{matrix}{{{f(n)} = \frac{{4\lbrack {{E\{ {{\overset{\sim}{y}}^{2}(n)} \} {{\overset{\sim}{y}}^{2}(n)}} - {E\{ {{\overset{\sim}{y}}^{4}(n)} \}}} \rbrack}\overset{\sim}{y}\quad (n)}{E^{3}\{ {{\overset{\sim}{y}}^{2}(n)} \}}},} \\{{{E\{ {{\overset{\sim}{y}}^{2}(n)} \}} = {{\beta \quad E\{ {{\overset{\sim}{y}}^{2}( {n - 1} )} \}} + {( {1 - \beta} ){{\overset{\sim}{y}}^{2}(n)}}}},{and}}\end{matrix} \\{{E\{ {{\overset{\sim}{y}}^{4}(n)} \}} = {{\beta \quad E\{ {{\overset{\sim}{y}}^{4}( {n - 1} )} \}} + {( {1 - \beta} ){{{\overset{\sim}{y}}^{4}(n)}.}}}}\end{matrix} & (5)\end{matrix}$

[0071] where β controls the smoothness of the moment estimates.

[0072] The LP analyzer 130 analyzes an input signal and provides the LPresidual output. The LP analyzer 130 can be a filter constrained to bean all-pole linear filter that performs a linear prediction of the nextsample as a weighted sum of past samples: $\begin{matrix}{{\hat{s}}_{n} = {\sum\limits_{i = 1}^{p}\quad {a_{i}s_{n - 1}}}} & (6)\end{matrix}$

[0073] Thus, the LP analyzer 130 has the transfer function:$\begin{matrix}{{A(z)} = \frac{1}{1 - {\sum\limits_{i = 1}^{p}\quad {a_{i}z^{- 1}}}}} & (7)\end{matrix}$

[0074] The LP analyzer 130 coefficients a, can be chosen to minimize themean square filter prediction error summed over the analysis window.

[0075] The LP synthesis filter 140 filters the acoustic reverberationoutput from the adaptive filter 110 and provides a processed outputsignal. The LP synthesis filter 140 can perform the inverse function ofthe LP analyzer 130.

[0076] While FIG. 1 is a block diagram illustrating components for theaudio enhancement system 100, it is to be appreciated that the audioenhancement system 100, the adaptive filter 110, the feedback component120, the LP prediction analyzer 130 and/or the LP synthesis filter 140can be implemented as one or more computer components, as that term isdefined herein. Thus, it is to be appreciated that computer executablecomponents operable to implement the audio enhancement system 100, theadaptive filter 110, the feedback component 120, the LP predictionanalyzer 130 and/or the LP synthesis filter 140 can be stored oncomputer readable media including, but not limited to, an ASIC(application specific integrated circuit), CD (compact disc), DVD(digital video disk), ROM (read only memory), floppy disk, hard disk,EEPROM (electrically erasable programmable read only memory) and memorystick in accordance with the present invention.

[0077] Turning to FIG. 2, an audio enhancement system 200 in accordancewith an aspect of the present invention is illustrated. For example, LPreconstruction artifacts can be reduced utilizing the system 200 at thesmall price of running two filters.

[0078] The system 200 includes a first adaptive filter 210, an LPanalyzer 220, a second adaptive filter 230 and a feedback component 240.It is to be appreciated that the first adaptive filter 210, the LPanalyzer 220, the second adaptive filter 230 and/or the feedbackcomponent 240 can be implemented as one or more computer components, asthat term is defined herein.

[0079] The first adaptive filter 210 filters an input signal based, atleast in part, upon a plurality of adaptive coefficients. The firstadaptive filter 210 provides a quality enhanced output (e.g., acousticreverberation reduced).

[0080] The LP analyzer 220 analyzes the input signal and provides alinear prediction residual output. The LP analyzer 220 can be a filterconstrained to be an all-pole linear filter that performs a linearprediction of the next sample as a weighted sum of past samplesemploying equations (6) and (7) above.

[0081] The second adaptive filter 230 filters the linear predictionoutput received from the LP analyzer 220 based, at least in part, uponthe plurality of adaptive coefficients. The second adaptive filter 230is adapted to modify at least one of the plurality of adaptivecoefficients based, at least in part, upon a feedback output from thefeedback component 240.

[0082] The second adaptive filter 230 can employ a filter technology(e.g., reverberation measuring) based on a non-linear function, forexample, the kurtosis metric. In the instance in which the secondadaptive filter 230 is adapted to maximize the kurtosis (J(n)) of aninput signal {tilde over (x)}(n), the second adaptive filter 230 canmodify the plurality of adaptive coefficients based, at least in part,upon equations (1), (2), (3) and (4) above. The second adaptive filter230 further provides an output to the feedback component 240.

[0083] The feedback component 240 provides the feedback output f(n)which is used by the second adaptive filter 230 to control filterupdates. The feedback output can be based, at least in part, upon anon-linear function of the output of the second adaptive filter 230. Forexample, the feedback output can be based, at least in part, upon theequation (5) above.

[0084] A multi-channel time-domain implementation extends directly fromthe system 200. As before, the objective is to maximize the kurtosis of{tilde over (y)}(n), the LP residual of y(n). In this case,${{y(n)} = {\sum\limits_{c = 1}^{C}\quad {{h_{c}^{T}(n)}\quad {x_{c}(n)}}}},$

[0085] where C is the number of channels. Extending the analysis of thesystem 200, the multi-channel update equations become:

h _(c)(n+1)=h _(c)(n)+μf(n){tilde over (x)} _(c)(n)  (8)

[0086] where the feedback function f(n) is computed as in (5) using themulti-channel y(n). To jointly optimize the filters, each channel can beindependently adapted, using substantially the same feedback function.

[0087] Direct use of the time-domain LMS-like adaptation described abovecan, in certain circumstances lead to slow convergence, or noconvergence at all under noisy situations, due, at least in part, tolarge variations in the eigenvectors of the autocorrelation matrices ofthe input signals. A frequency-domain implementation can reduce thisproblem.

[0088] Referring next to FIG. 3, a frequency domain audio enhancementsystem 300 in accordance with an aspect of the present invention isillustrated. The system 300 includes a first adaptive filter 310, an LPanalyzer 320, a second adaptive filter 330 and a feedback component 340.

[0089] The system 300 uses a subband adaptive filtering structure based,for example, on the modulated complex lapped transform (MCLT). Since thesubband signal has an approximately flat spectrum, faster convergenceand/or reduced sensitivity to noise, for example, can be achieved.

[0090] The first adaptive filter 310 includes a frequency transform 312,a plurality of adaptive coefficients 314 and an inverse frequencytransform 316. The frequency transform 312 performs a frequency domaintransform of an input signal. In one example, the frequency transform312 employs an MCLT that decomposes the input signal into M complexsubbands. However, it is to be appreciated that any suitable frequencydomain transform can be employed by the frequency transform 312 inaccordance with the present invention.

[0091] The plurality of adaptive coefficients 314 are used by the firstadaptive filter to filter the input signal. The inverse frequencytransform 316 performs an inverse frequency domain transform of thefiltered frequency transformed input signal. For example, the inversefrequency transform 316 can perform an inverse MCLT.

[0092] The LP analyzer 320 analyzes the input signal and provides alinear prediction residual output. The LP analyzer 320 can be a filterconstrained to be an all-pole linear filter that performs a linearprediction of the next sample as a weighted sum of past samplesemploying equations (6) and (7) above.

[0093] The second adaptive filter 330 includes a frequency transform332, a plurality of adaptive coefficients 334 and an inverse frequencytransform 336. The frequency transform 332 performs a frequency domaintransform of the linear prediction output. The second adaptive filter330 filters the frequency domain transformed linear prediction outputbased, at least in part, upon the plurality of adaptive coefficients334. The second adaptive filter 330 modifies at least one of theplurality of adaptive coefficients 334 based, at least in part, upon afeedback output from the feedback component 340. The second adaptivefilter 334 further provides an output.

[0094] In one example, the frequency transform 332 employs an MCLT thatdecomposes the input signal into M complex subbands. However, it is tobe appreciated that any suitable frequency domain transform can beemployed by the frequency transform 332 in accordance with the presentinvention.

[0095] In another example, the frequency transform 312 employs an MCLTthat decompose the input signal into M complex subbands. To determine M,the tradeoff that larger M are desired to whiten the subband spectra,whereas smaller M are desired to reduce processing delay is considered.For example, a good compromise can be to set M such that the framelength is about 20-40 ms. A subband s of a channel c is processed by acomplex FIR adaptive filter with L taps 314, H_(c) (s,m), where m is theMCLT frame index. By considering that the MCLT approximately satisfiesthe convolution properties of the FFT, the update equations describedabove can be mapped to the frequency domain, generating the followingupdate equation:

H _(c)(s,m+1)=H _(c)(s,m)+μF(s,m){tilde over (X)} _(c)*(s,m)  (9)

[0096] where the superscript * denotes complex conjugation.

[0097] The feedback component 340 provides the feedback output based, atleast in part, upon a non-linear function of the output of the secondadaptive filter 330. Unlike in a standard LMS formulation, theappropriate feedback function F(s, m) cannot be computed in thefrequency domain. To compute the MCLT-domain feedback function F(s, m),the reconstructed signal {tilde over (y)}(n) is generated and f(n) iscomputed from equation (5). F(s, m) is then computed from f(n), forexample, using the MCLT. The overlapping nature of the MCLT introduces aone-frame delay in the computation of F(s, m). Thus, to maintain anappropriate approximation of the gradient, the previous input block isused in the update equation (9), generating the final update equation:

H _(c)(s,m+1)=H _(c)(s,m)+μF(s,m−1){tilde over (X)} _(c)*(s,m−1).  (10)

[0098] Assuming the learning gains is small enough, the extra delay inthe update equation above will introduce a very small error in the finalconvergence of the filter. As before, the updated plurality of adaptivecoefficients 334 of the second adaptive filter 330 are then copied tothe plurality of adaptive coefficients 314 of the first adaptive filter310.

[0099] It is to be appreciated that the first adaptive filter 310, theLP analyzer 320, the second adaptive filter 330 and/or the feedbackcomponent 340 can be implemented as one or more computer components, asthat term is defined herein.

[0100] Turning next to FIG. 4, a multi-channel audio enhancement system400 in accordance with an aspect of the present invention isillustrated. The system 400 includes a first channel frequency domainaudio enhancement system 410 ₁ through a Gth channel frequency domainaudio enhancement system 410 _(G), G being an integer greater to orequal to two. The first channel frequency domain audio enhancementsystem 410 ₁ through the Gth channel frequency domain audio enhancementsystem 410 _(G) can be referred to collectively as the frequency domainaudio enhancement systems 410. The system 400 further includes a firstsumming component 420, a second summing component 430 and a feedbackcomponent 440.

[0101] The frequency domain audio enhancement systems 410 include afirst adaptive filter 310, an LP analyzer 320 and a second adaptivefilter 330.

[0102] The first summing component 420 sums the outputs of the firstadaptive filters 310 of the frequency domain audio enhancement system410 and provides a reverberation reduced output y(n).

[0103] The second summing component 430 sums the outputs of the secondadaptive filters 330 of the frequency domain audio enhancement system410 and provides an output to the feedback component 440.

[0104] The feedback component 440 provides a feedback output based, atleast in part, upon a non-linear function of the output of the secondsumming component 430. Operation of the feedback component 440 can besimilar to the feedback component 340 above.

[0105] It is to be appreciated that the channel frequency domain audioenhancement systems 410, the first summing component 420, the secondsumming component 430 and/or the feedback component 440 can beimplemented as one or more computer components, as that term is definedherein.

[0106] Referring next to FIG. 5, a noise reduction system 500 inaccordance with an aspect of the present invention is illustrated. Thesystem 500 includes a first statistics component 510, a secondstatistics component 520 and a spatial filter 530. Optionally, thesystem 500 can include a voice activity detector 540.

[0107] The system 500 receives a plurality of input signals (e.g., frommicrophones). FIG. 5 includes four input signals (MIC 1, MIC 2, MIC 3and MIC 4) for purposes of illustration; however, it is to beappreciated that the present invention is not intended to be limited toany specific number of input signals.

[0108] The first statistics component 510 stores statistics associatedwith a first portion or type of an input signal (e.g., noise statisticsassociated with a noise portion of an input signal). The secondstatistics component 520 (e.g., second signal statistics component)stores statistics associated with a second portion or type of the inputsignal (e.g., signal+noise statistics associated with a signal and noiseportion of the input signal). The spatial filter 530 provides an outputsignal, the output signal being based, at least in part, upon a filteredinput signal, the filtering being based, at least in part, upon aweighted error calculation of the first statistics (e.g., noisestatistics) and the second statistics (e.g., signal+noise statistics).As described below, it is to be appreciated that the spatial filter 530of the system 500 can be a fixed filter or an adaptive filter.

[0109] The voice activity detector 540 provides information to the firststatistic component 510 and/or the second statistics component 520based, at least in part, upon the output signal of the spatial filter530. The voice activity detector 540 detects when substantially only thefirst portion or type of the input signal (e.g., noise) is present(e.g., silent period(s)) and provides the input signal to the firststatistics component 510. When, for example, speech is present, possiblywith noise, the input signal can be provided to the second statisticscomponent 520 by the voice activity detector 540.

[0110] To simplify the notation, an input vector x(n) is formed byreplicating the input samples as many times as filter taps that will usethat sample, the input vector x(n), contains samples from substantiallyall input channels, and from current and past (or “future”) sample ofeach of those channels. So, for example, if one microphone signal isdenoted as x₁(n), and another microphone signal as x₂(n), an inputvector x(n) for a 3-tap per channel array can be composed as:

x(n)=[x ₁(n−1)x ₁(n)x ₁(n+1)x ₂(n−1)x ₂(n)x ₂(n+1)]  (11)

[0111] Therefore, at a time instant n, x(n) is a T×1 vector, where T isthe number of total taps in the filter (e.g., generally the number ofchannels multiplied by the number of taps used for each channel).Furthermore, for simplification of explanation, the time index n will bedropped, x will be used to denote the input vector. A similar notationwill be employed for other vectors and variables.

[0112] It can be assumed that noise is linearly added to the desiredsignal. In other words, the input signal can be written as:

x=s+n  (12)

[0113] where s is the speech component of the signal and n is theadditive ambient or interfering noise. Furthermore, it can be assumedthat the noise is statistically independent from the desired signal,although it might be correlated between different microphones.

[0114] The basic hypothesis is that the desired signal is essentiallythe same on substantially all channels, possibly with the exception of adelay, or maybe different room-transfer functions. It is desired tocompute a filter w, which will produce a single-channel output y, givenby:

y=w.x  (13)

[0115] where w is the 1×T filter vector, and which minimizes anappropriate error measure between y and a desired signal d.

[0116] The spatial filter 530 can utilize a Wiener filter. In a Wienerfilter, the received signal is filtered by a filter computed as:

w _(opt)=(R _(ss) +R _(nn))⁻¹(E{sx}),  (14)

[0117] where R_(ss) is the autocorrelation matrix for the desired s, forexample, stored in the second statistics component 520, R_(nn) is thecorrelation matrix for the noise component n stored in the firststatistics component 510, and E{sx} is the cross correlation between thedesired signal s and the received signal x.

[0118] The above can be directly generalized to a multi-channelsituation, by simply forming a vector containing samples from several ofthe channels. Wiener filtering can be shown to be optimum in minimizingMSE between the desired signal and the output of the filter.

[0119] For example, in most practical situations, the filter output willnot be the same as the desired signal, but a distorted version of thedesired signal. The distortion introduced by the Wiener filtering can beseparated into two parts: residual noise (e.g., remaining noise that wasnot removed by the filter), and signal distortion (e.g., modificationsin the original signal introduced by the filter in trying to remove thenoise). The human ear is more sensitive to independent noise. Therefore,Wiener filtering is not “human” optimum, in that it gives the sameweight to these two kinds of distortion. Thus, the spatial filter 530can utilize an improved filter, which accounts for these twodifferently. Given an error criteria, defined as:

ε=E{(w.s−d)²+β(w.n)²}.  (15)

[0120] where β is a weighting parameter for the independent noisecomponent. Thus, it can be shown that an improved filter is:

w _(opt)=(R _(ss) +βR _(nn))⁻¹(E{dx}}.  (16)

[0121] This modification in the Wiener filter produces a sub-optimalfilter in terms of MSE, but it can produce significantly better results,when judged on the subjective criteria of perceived quality, or whenusing the error criteria defined above.

[0122] In many situations, statistics for the desired signal are notavailable. In that case, the above equation can be modified:

w _(opt)=(R _(XX) +ρR _(nn))⁻¹(E{x _(o) x}−E{n _(o) n})  (17)

[0123] where ρ=β−1. In this case the signal component received in one ofthe microphones (e.g., x_(o)) is selected as the desired signal. Thesubtracted term E{n_(o)n} makes sure the noise present in that samemicrophone is not incorporated into the desired signal. Note theformulation in Equation (17) is appropriate for use with the system 500,as the statistics are based on the first portion of the input signal(e.g., noise) and the second portion of the input signal (e.g.,noise+signal).

[0124] In one example, the signal and the noise have characteristicsthat are known beforehand. Accordingly, the spatial filter 530 can becomputed a priori, and therefore be a fixed filter, with the advantageof reduced computational complexity.

[0125] In another example, advance knowledge of the signal and noisecharacteristics is minimal and/or may be time-varying. In this case, thenoise statistic and the signal+noise statistics are estimated and thefilter is computed in an adaptive way. This can be accomplished byincluding the voice activity detector 540, and re-estimating thestatistic(s) periodically. The spatial filter 530 is then updatedperiodically, at the same or lower frequency than the statistics update.In one example, the spatial filter 530 is updated less frequently inorder to reduce computational overhead. In another example, the filtersare adapted in an LMS-like fashion, as it will be explained later inassociation with FIG. 7.

[0126] It is to be appreciated that the first statistics component 510,the second statistics component 520, the spatial filter 530 and/or thevoice activity detector 540 can be implemented as one or more computercomponents, as that term is defined herein.

[0127] Turning to FIG. 6, a frequency domain noise reduction system 600in accordance with an aspect of the present invention is illustrated.The system 600 includes a plurality of frequency transforms 610, a voiceactivity detector 620, a filter component 630 and an inverse frequencytransform 640.

[0128] It is often beneficial for computational and/or performancereasons to compute and process the filters above described in thefrequency domain. Several methods can be used to that end, in particulara modulated complex lapped transform (MCLT). In this example, theplurality of frequency transform 610 implement an MCLT of input signals.The filter component 630 may employ the filtering described with respectto FIG. 5 or 7, except that the filtering is performed, for example, foreach band. In other words, a filter is computed for each band, and thatfilter is used to process the samples for that band coming out from themicrophones.

[0129] It is to be appreciated that the plurality of frequencytransforms 610, the voice activity detector 620, the filter component630 and/or the inverse frequency transform 640 can be implemented as oneor more computer components, as that term is defined herein.

[0130] The matrix inversion implied in Equation (17) can becomputationally demanding, even if one uses the properties of thecorrelation matrix. One possibility to reduce complexity is to use anLMS adaptive filter.

[0131] An LMS adaptive filter converges to the optimum filter w, whichminimizes the traditional mean squared error (MSE) between the output ofthe filter, and the desired signal. For example, given an input signalz, and an desired signal d, this adaptive filter will converge to theoptimum filter, which is:

w=(R _(zz))⁻¹ E{dz}.  (18)

[0132] where R_(zz) is the autocorrelation matrix for the signal z.

[0133] This equation is similar to Equation (17), with two exceptions:first, the desired signal is not utilized; and, second, it is minimizingthe wrong error criteria. A LMS formulation cannot, therefore, be useddirectly. According, “artificial” signals are created that will make theLMS filter converge to our desired filter. In other words, input anddesired signals are synthesized that will generally make the LMS filterconverge to the filter in Equation (17).

[0134] Since the signal and noise are independent, the first term can bemade the same by adding some noise to the signal, for example, by makingz=x+λn, where n is noise obtained from the “noise buffer”, and λ is ascalar gain. Using the fact that x and n are independent, by makingλ=sqrt(ρ), results in R_(zz)=R_(xx)+ρR_(nn). Therefore, this choice forz makes the first part of equations (17) and (18) be substantially thesame.

[0135] In order to make the second part of equations (17) and (18)match, the input signal x₀ cannot be used as the desired signal, becausethe term −E{n_(o)n} would not be present. To correct for that term, thedesired signal d=x₀−λ⁻¹n₀ is used instead. This choice yields:

E{dz}=E{(x ₀−λ⁻¹ n ₀)(x+λn)}=E{x ₀ x−λ ⁻¹ n ₀ x+x ₀ λn−λ ⁻¹ n ₀λn}  (19)

[0136] Since n and x are independent, the expected value of crossproducts are zero, thus leading to:

E{dz}=E{(x ₀−λ⁻¹ n ₀)(x+λn)}=E{x ₀ x}−E{n ₀ n}  (20)

[0137] And therefore this makes the second part of equation (17) and(18) match. Accordingly, this particular choice of input and desiredsignals will make the LMS filter converge to the desired filter.

[0138] Turning next to FIG. 7, an LMS-based noise reduction system 700in accordance with an aspect of the present invention is illustrated.The system 700 includes a signal+noise buffer 710, a noise buffer 720, asignal composer 730, a filter 740, an LMS filter 750 and differentialcomponent 760.

[0139] The algorithm described with respect to FIGS. 5 and 6 works basedon the differences between the statistics of the signal and noise. Thesestatistics are computed in a two-phase process (“noise only” and“signal+noise”), and stored as internal states in the system,represented by the two matrices (e.g., one for each phase). Theadaptation is therefore based on using the incoming signal to update oneof these matrices at a time, according to the presence (or absence) ofthe desired signal. In contrast, an LMS-based filter doesn't have thesame two separate internal states matrices. It usually incorporatesinput data directly into the filter coefficients, and therefore does notallow for this two-phase process.

[0140] To circumvent this problem, it is first noted note that the datacontained in the statistics matrices is essentially a subset of theinformation contained in the corresponding signals, from which thematrices were computed. So, instead of storing the two statisticsmatrices, the data itself is stored in two separate buffers (e.g,circular), the signal+noise buffer 710 and the noise buffer 720, whichare used to directly adapt the LMS filter 750. More precisely, theincoming data is classified, for example, by a voice activity detector(not shown) as either “signal+noise” or “noise,” and stored in theappropriate buffer, signal+noise buffer 710 and noise buffer 720,respectively, for later usage. For example, a signal presence flag orother signal can be received from the voice activity detector.

[0141] A synthetic input signal z and its associated desired signal daregenerated by the signal composer 730 by adding data from thesignal+noise buffer 710 and/or the noise buffer 720 to the input data.This synthetic signal is used to adapt the LMS filter 750. The filtercoefficients are copied (e.g., continuously) to the filter 740, whichdirectly processes the input signal.

[0142] This approach reduces the need for calibration signal(s) as withconventional system(s), thus making the overall system more robust tochanges in the environment, the speaker, the noise, and/or themicrophones. Also, the careful choice of synthetic signals—as describedbelow—avoids the need to acquire a “clean” signal. FIG. 7 illustrates anLMS-based noise reduction system for a single frequency band.

[0143] A factor in achieving the desired results is, of course, how tocompose the signals that are used to adapt the LMS filter. In oneexample, the composed signals are based on the optimization criteriadiscussed before, and assuming the signal+noise buffer 710 and/or thenoise buffer 720 are short enough so that the signals contained in eachare representative of the two classes. A two-phase composition can beemployed: if speech is detected in the incoming signal x, more noise isadded (from the noise buffer 720), to facilitate achieving the desiredextra noise attenuation. In other words, the input signal z to theadaptive filter is computed as:

z=x+ρn,  (21)

[0144] and the desired signal is set to: $\begin{matrix}{d = {x_{0} - {\frac{1}{\rho}{n_{0}.}}}} & (22)\end{matrix}$

[0145] Note the negative term added to the desired noise which is used,for example, to prevent the small amount of noise present in x₀ frombeing preserved. Instead, the filter will converge to an unbiasedestimate of the filter. On the other hand, when substantially no speechis detected in the incoming signal, a small amount of signal is added,to avoid converging to a signal-canceling filter:

z=ρx+s,  (23)

[0146] and set the associated desired signal to: $\begin{matrix}{d = {{{- \frac{1}{\rho}}x_{0}} + {s_{0}.}}} & (24)\end{matrix}$

[0147] Note again the negative term in the desired signal, which hassubstantially the same purpose as described before. Note also that theinput signal is scaled in such a way that the energy at the input of thefilter does not vary significantly between speech and silence periods.

[0148] Finally, the signal presence flag provided by the voice activitydetector (not shown) is used, for example, for two purposes. A first useis to decide between using equations (21) and (22) or equations (23) and(24) to synthesize the signals. While the algorithm adds differentsignals—depending on the signal presence flag—this is not actuallycritical. An eventual misclassification may slow down convergence, butwill not have significant consequences otherwise. A second use of thesignal presence flag is to decide in which buffer to store the signal.In contrast with the first case, a misclassification in the second usemay degrade performance significantly. Including part(s) of the desiredsignal in the noise buffer may lead to signal cancellation. To alleviatethis problem, two distinct speech activity detectors can be used, onefor each of these uses. Since the second use of the signal presence flagis not in the direct signal path, a more robust, long-delay voiceactivity detector can be used to decide in which buffer to store theincoming signal. This voice activity detector can have a “not-sure”region, where the signal is not stored in either buffer. Thus,adaptation can be based, at least in part upon, data stored in thesystem 700 in a different order than the time-of-arrival order.

[0149] It is to be appreciated that the signal+noise buffer 710, thenoise buffer 720, the signal composer 730, the filter 740, the LMSfilter 750 and/or the differential component 760 can be implemented asone or more computer components, as that term is defined herein.

[0150] Referring next to FIG. 8, a signal enhancement system 800 inaccordance with an aspect of the present invention is illustrated. Thesystem 800 includes a frequency transform 810, a voice activity detector820, a noise buffer 830, a signal+noise buffer 840, a filter 850, anoise adaptive filter 860, a reverberation adaptive filter 870, aninverse frequency transform 872, a noise feedback component 874 and areverberation feedback component 888.

[0151] The frequency transform 810 performs a frequency domain transformof an input signal. In one example, the frequency transform 810 employsan MCLT that decomposes the input signal into M complex subbands.However, it is to be appreciated that any suitable frequency domaintransform can be employed by the frequency transform 810 in accordancewith the present invention.

[0152] The voice activity detector 820 provides information to the noisebuffer 830 and/or the signal+noise buffer 840 based, at least in part,upon the input signal. The voice activity detector 820 detects whensubstantially only noise is present (e.g., silent period(s)) andprovides the input signal to the noise buffer 830. When speech ispresent, possibly with noise, the input signal can be provided to thenoise+signal buffer 840 by the voice activity detector 820. In oneexample, the voice activity 820 discards sample(s) of the input signalwhich it is unable to classify as noise or “signal+noise”.

[0153] The signals stored in the noise buffer 830 are used to train thenoise adaptive filter 860 while the signal stored in the noise+signalbuffer 840 are used train the reverberation adaptive filter 870. Byusing signals from the noise buffer 830 and/or the noise+signal buffer840, the noise adaptive filter 860 and/or the reverberation adaptivefilter are trained on a signal that is slightly older. In most cases,the signal and noise characteristics do not change too abruptly, andthis delay does not constitute a problem. Note also that the rate ofadaptation can be the same, higher or lower than the data rate. By samedata rate it is meant that one sample from each buffer is processed foreach sample that is received as input. A lower adaptation rate reducescomplexity, while a higher rate can improve convergence and/or trackingof the environment change(s).

[0154] The filter 850 filters the frequency transform input signalreceived from the frequency transform 810 based, at least in part, upona plurality of adaptive coefficients. The filter 850 provides a filteredoutput to the inverse frequency transform 872 which performs an inversefrequency transform (e.g., inverse MCLT) and provides an acousticenhancement signal output. The plurality of adaptive coefficientsutilized by the filter 850 are modified by the noise adaptive filter 860and/or the reverberation adaptive filter 870.

[0155] The noise adaptive filter 860 filters the signals stored in thenoise buffer 830 based, at least in part, upon the plurality of adaptivecoefficients. The noise adaptive filter 860 is adapted to modify atleast one of the plurality of adaptive coefficients based, at least inpart, upon a feedback output from the noise feedback component 874.

[0156] The noise adaptive filter 860 can employ the improved Wienerfilter technique(s) described above. In the instance in which the noiseadaptive filter 860 is adapted to modify the plurality of adaptivecoefficients based, at least in part, upon equations (16) and (17)above. The noise adaptive filter 860 further provides an output to thenoise feedback component 874.

[0157] The noise feedback component 874 provides the noise reductionfeedback output based, at least in part, upon a weighted errorcalculation of the output of the noise reduction adaptive filter.

[0158] The reverberation adaptive filter 870 filters the signals storedin the noise+signal buffer 840 based, at least in part, upon theplurality of adaptive coefficients. The reverberation adaptive filter870 is adapted to modify at least one of the plurality of adaptivecoefficients based, at least in part, upon a feedback output from thereverberation feedback component 888.

[0159] The reverberation adaptive filter 870 employs a reverberationmeasuring filter technology based on a non-linear function, for example,the kurtosis metric. In the instance in which the reverberation adaptivefilter 870 is adapted to maximize the kurtosis (J(n)) of an input signal{tilde over (x)}(n), the reverberation adaptive filter 870 can modifythe plurality of adaptive coefficients based, at least in part, uponequations (1), (2), (3) and (4) above. The reverberation adaptive filter870 further provides an output to the reverberation feedback component888.

[0160] The reverberation feedback component 888 provides the feedbackoutput f(n) which is used by the reverberation adaptive filter 870 tocontrol filter updates. The feedback output can be based, at least inpart, upon a non-linear function of the output of the reverberationadaptive filter 870. For example, the feedback component 894 can employ,at least in part, equation (5) above.

[0161] It is to be appreciated that the frequency transform 810, thevoice activity detector 820, the noise buffer 830, the signal+noisebuffer 840, the filter 850, the noise adaptive filter 860, thereverberation adaptive filter 870, the inverse frequency transform 872,the noise feedback component 874 and/or the reverberation feedbackcomponent 888 can be implemented as one or more computer components, asthat term is defined herein.

[0162] In the examples previously described, the input to the noiseadaptive filter 860 and/or the reverberation adaptive filter 870 filteris the LPC residual of the corresponding signal. This can have, forexample, two main objectives; first, it equalizes the signal, improvingconvergence. Second, it provides the output of the filter is already inthe LPC residual of the filtered signal. This second attribute isnecessary because the (reverberation reducing) nonlinear feedbackfunction is based on maximizing the “impulse train-like” characteristicsof this LPC residual. An alternative to filtering the input signal,which may be advantageous in some situations is provided below.

[0163] In one example, to reduce computational complexity, the trainingdata is not filtered by an LPC filter. Instead, the (single channel)output of the filter is filtered twice by the LPC filter. The secondfilter uses the symmetric reflection of the LPC filter. Not does onlythis reduces complexity, but it also allows the LPC filter to becomputed on the noise reduced signal, improving performance in noisysituations. This can be seen as incorporating the LPC filtering withinthe feedback function.

[0164] In view of the exemplary systems shown and described above,methodologies that may be implemented in accordance with the presentinvention will be better appreciated with reference to the flow chartsof FIGS. 9, 10, 11, 12, 13 and 14. While, for purposes of simplicity ofexplanation, the methodologies are shown and described as a series ofblocks, it is to be understood and appreciated that the presentinvention is not limited by the order of the blocks, as some blocks may,in accordance with the present invention, occur in different ordersand/or concurrently with other blocks from that shown and describedherein. Moreover, not all illustrated blocks may be required toimplement the methodologies in accordance with the present invention.

[0165] The invention may be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more components. Generally, program modules include routines,programs, objects, data structures, etc. that perform particular tasksor implement particular abstract data types. Typically the functionalityof the program modules may be combined or distributed as desired invarious embodiments.

[0166] Turning to FIG. 9, a method 900 for reducing acousticreverberation in accordance with an aspect of the present invention isillustrated. At 910, an acoustic reverberation reduced output based, atleast in part, upon a plurality of adaptive coefficients is provided. At920, a coefficient adaptation feedback output is provided based, atleast in part, upon a non-linear function of the acoustic reverberationreduced output. For example, the non-linear function can be based, atleast in part, upon maximization of kurtosis. At 930, at least one ofthe plurality of adaptive coefficient is modified based, at least inpart, upon a feedback output.

[0167] Next, referring to FIG. 10, a method 1000 for reducing acousticreverberation in accordance with an aspect of the present invention isillustrated. At 1010, an input signal is filtered based, at least inpart, upon a plurality of adaptive coefficients. At 1020, an acousticreverberation reduced output is provided based, at least in part, uponthe filtered input signal. Next, at 1030, a linear prediction residualoutput is provided based on the input signal. At 1040, the linearprediction residual output is filtered based, at least in part, upon theplurality of adaptive coefficients. At 1050, a feedback output (e.g.,for adapting at least one of the plurality of adaptive coefficients) isprovided based, at least in part, upon a non-linear function (e.g.,based, at least in part, upon maximization of kurtosis) of the filteredlinear prediction residual output. At 1060, at least one of theplurality of adaptive coefficients is modified based, at least in part,upon the feedback output.

[0168] Turning next to FIG. 11, a method 1100 for reducing acousticnoise in accordance with an aspect of the present invention isillustrated. At 1010, noise statistics associated with a noise portionof an input signal are computed. At 1020, signal+noise statistics with asignal and noise portion of the input signal are computed. At 1030, theinput signal is filtered based, at least in part, upon a filter computedbased on optimizing a weighted error criteria, based on calculation ofthe noise statistics and the signal+noise statistics.

[0169] Referring next to FIG. 12, a method 1200 for reducing acousticnoise in accordance with an aspect of the present invention isillustrated. At 1210, a noise portion of an input signal is stored. At1220, a signal+noise portion of the input signal is stored. Next, at1230, the input signal is filtered based, at least in part, upon aplurality of adaptive coefficients. At 1240, a synthetic input signaland a synthetic desired signal are generated based, at least in part,upon at least one of the stored noise portion of the input signal andthe stored signal+noise portion of the input signal. For example, thesynthetic signals can be chosen, at least in part, to optimize aweighted error criteria of residual noise and signal distortion.

[0170] At 1250, an LMS filter output is provided based, at least inpart, upon the plurality of adaptive coefficients. At 1260, a feedbackoutput is provided based, at least in part, upon a difference betweenthe LMS filter output and the synthetic desired signal At 1270, at leastone of the plurality of adaptive coefficients is modified based, atleast in part, upon the feedback output.

[0171] Turning to FIGS. 13 and 14, a method 1300 for enhancing anacoustic signal in accordance with an aspect of the present invention isillustrated. At 1310, noise information associated with a noise portionof an input signal is stored. At 1320, signal+noise information with asignal and noise portion of the input signal is stored. At 1330, theinput signal is filtered based, at least in part, upon a plurality ofadaptive coefficients.

[0172] At 1340, a noise adaptive filter output signal is provided based,at least in part, upon the stored noise information and the plurality ofadaptive coefficients. At 1350, a noise feedback output is providedbased, at least in part, upon a measure of the residual noise. At 1360,at least one of the plurality of adaptive coefficients is modifiedbased, at least in part, upon the noise feedback output.

[0173] At 1370, a reverberation filter output is provided based, atleast in part, upon the stored signal+noise information and theplurality of adaptive coefficients. At 1380, a reverberation feedbackoutput is provided based, at least in part, upon a non-linear functionof the output of the reverberation adaptive filter. At 1390, at leastone of the plurality of adaptive coefficients is modified based, atleast in part, upon the reverberation feedback output.

[0174] It is to be appreciated that the system and/or method of thepresent invention can be utilized in an overall signal enhancementsystem. Further, those skilled in the art will recognize that the systemand/or method of the present invention can be employed in a vast arrayof acoustic applications, including, but not limited to,teleconferencing and/or speech recognition. It is also to be appreciatedthat the system and/or method of the present invention can be applied tohandle multi-channel inputs (based on a plurality of input devices, forexample, microphones).

[0175] In order to provide additional context for various aspects of thepresent invention, FIG. 15 and the following discussion are intended toprovide a brief, general description of a suitable operating environment1510 in which various aspects of the present invention may beimplemented. While the invention is described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more computers or other devices, those skilled in the art willrecognize that the invention can also be implemented in combination withother program modules and/or as a combination of hardware and software.Generally, however, program modules include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular data types. The operating environment 1510 is onlyone example of a suitable operating environment and is not intended tosuggest any limitation as to the scope of use or functionality of theinvention. Other well known computer systems, environments, and/orconfigurations that may be suitable for use with the invention includebut are not limited to, personal computers, hand-held or laptop devices,multiprocessor systems, microprocessor-based systems, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include the above systems ordevices, and the like.

[0176] With reference to FIG. 15, an exemplary environment 1510 forimplementing various aspects of the invention includes a computer 1512.The computer 1512 includes a processing unit 1514, a system memory 1516,and a system bus 1518. The system bus 1518 couples system componentsincluding, but not limited to, the system memory 1516 to the processingunit 1514. The processing unit 1514 can be any of various availableprocessors. Dual microprocessors and other multiprocessor architecturesalso can be employed as the processing unit 1514.

[0177] The system bus 1518 can be any of several types of busstructure(s) including the memory bus or memory controller, a peripheralbus or external bus, and/or a local bus using any variety of availablebus architectures including, but not limited to, 15-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

[0178] The system memory 1516 includes volatile memory 1520 andnonvolatile memory 1522. The basic input/output system (BIOS),containing the basic routines to transfer information between elementswithin the computer 1512, such as during start-up, is stored innonvolatile memory 1522. By way of illustration, and not limitation,nonvolatile memory 1522 can include read only memory (ROM), programmableROM (PROM), electrically programmable ROM (EPROM), electrically erasableROM (EEPROM), or flash memory. Volatile memory 1520 includes randomaccess memory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such assynchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM),double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SynchlinkDRAM (SLDRAM), and direct Rambus RAM (DRRAM).

[0179] Computer 1512 also includes removable/nonremovable,volatile/nonvolatile computer storage media. FIG. 15 illustrates, forexample a disk storage 1524. Disk storage 1524 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 1524 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage devices 1524 to the system bus 1518, aremovable or non-removable interface is typically used such as interface1526.

[0180] It is to be appreciated that FIG. 15 describes software that actsas an intermediary between users and the basic computer resourcesdescribed in suitable operating environment 1510. Such software includesan operating system 1528. Operating system 1528, which can be stored ondisk storage 1524, acts to control and allocate resources of thecomputer system 1512. System applications 1530 take advantage of themanagement of resources by operating system 1528 through program modules1532 and program data 1534 stored either in system memory 1516 or ondisk storage 1524. It is to be appreciated that the present inventioncan be implemented with various operating systems or combinations ofoperating systems.

[0181] A user enters commands or information into the computer 1512through input device(s) 1536. Input devices 1536 include, but are notlimited to, a pointing device such as a mouse, trackball, stylus, touchpad, keyboard, microphone, joystick, game pad, satellite dish, scanner,TV tuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1514through the system bus 1518 via interface port(s) 1538. Interfaceport(s) 1538 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1540 usesome of the same type of ports as input device(s) 1536. Thus, forexample, a USB port may be used to provide input to computer 1512, andto output information from computer 1512 to an output device 1540.Output adapter 1542 is provided to illustrate that there are some outputdevices 1540 like monitors, speakers, and printers among other outputdevices 1540 that require special adapters. The output adapters 1542include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1540and the system bus 1518. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1544.

[0182] Computer 1512 can operate in a networked environment usinglogical connections to one or more remote computers, such as remotecomputer(s) 1544. The remote computer(s) 1544 can be a personalcomputer, a server, a router, a network PC, a workstation, amicroprocessor based appliance, a peer device or other common networknode and the like, and typically includes many or all of the elementsdescribed relative to computer 1512. For purposes of brevity, only amemory storage device 1546 is illustrated with remote computer(s) 1544.Remote computer(s) 1544 is logically connected to computer 1512 througha network interface 1548 and then physically connected via communicationconnection 1550. Network interface 1548 encompasses communicationnetworks such as local-area networks (LAN) and wide-area networks (WAN).LAN technologies include Fiber Distributed Data Interface (FDDI), CopperDistributed Data Interface (CDDI), Ethernet/IEEE 1502.3, Token Ring/IEEE1502.5 and the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

[0183] Communication connection(s) 1550 refers to the hardware/softwareemployed to connect the network interface 1548 to the bus 1518. Whilecommunication connection 1550 is shown for illustrative clarity insidecomputer 1512, it can also be external to computer 1512. Thehardware/software necessary for connection to the network interface 1548includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

[0184] What has been described above includes examples of the presentinvention. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe present invention, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the presentinvention are possible. Accordingly, the present invention is intendedto embrace all such alterations, modifications and variations that fallwithin the spirit and scope of the appended claims. Furthermore, to theextent that the term “includes” is used in either the detaileddescription or the claims, such term is intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. An audio enhancement system, comprising: anadaptive filter that filters an input signal, at least in part, upon aplurality of adaptive coefficients, the adaptive filter modifying atleast one of the plurality of adaptive coefficients based, at least inpart, upon a feedback output, the adaptive filter providing a qualityenhanced output; and, a feedback component that provides the feedbackoutput based, at least in part, upon a non-linear function of thequality enhanced output.
 2. The audio enhancement system of claim 1, thenon-linear function being based on increasing a non-linear measure ofspeechness.
 3. The audio enhancement system of claim 2, the non-linearmeasure of speechness being based on maximizing kurtosis of a LPCresidual of the quality enhanced output.
 4. The audio enhancement systemof claim 2, the non-linear measure of speechness being based onmaximizing the impulse-train like characteristic of a LPC residual ofthe quality enhanced output.
 5. The audio enhancement system of claim 1,the input signal being based on one microphone.
 6. The audio enhancementsystem of claim 1, the input signal being based on at least twomicrophones.
 7. An audio enhancement system, comprising: a firstadaptive filter that filters an input signal based, at least in part,upon a plurality of adaptive coefficients, the first adaptive filterproviding a quality enhanced output; a second adaptive filter thatfilters the input signal based, at least in part, upon the plurality ofadaptive coefficients, the second adaptive filter modifying at least oneof the plurality of adaptive coefficients based, at least in part, upona feedback output, the second adaptive filter further providing anoutput; and, a feedback component that provides the feedback outputbased, at least in part, upon a non-linear function of the output of thesecond adaptive filter.
 8. The audio enhancement system of claim 7, thesecond adaptive filter being adapted based on a delayed version of theinput signal.
 9. The audio enhancement system of claim 7, the secondadaptive filter being adapted based on a modified version of the inputsignal.
 10. The audio enhancement system of claim 7, the second adaptivefilter being adapted based on an equalized version of the input signal.11. The audio enhancement system of claim 7, the second adaptive filterrunning at a different data rate than the first adaptive filter.
 12. Theaudio enhancement system of claim 111, the second adaptive filterrunning at a lower data rate than the first adaptive filter.
 13. Theaudio enhancement system of claim 11, the second adaptive filter runningat a higher data rate than the first adaptive filter.
 14. The audioenhancement system of claim 7, the second adaptive filter using datastored in the system.
 15. The audio enhancement system of claim 14, thesecond adaptive filter using the data stored in the system more thanonce.
 16. The audio enhancement system of claim 14, the second adaptivefilter using the data stored in the system in a different order than thetime-of-arrival order.
 17. The audio enhancement system of claim 7, theadaptive filter using data modified to edit out non-voiced parts of theinput signal.
 18. The audio enhancement system of claim 7, furthercomprising a plurality of second adaptive filters.
 19. The audioenhancement system of claim 18, the first adaptive filter and theplurality of second adaptive filters employing the same plurality ofadaptive coefficients, the plurality of adaptive coefficients beingmodified, at least in part, based on an output of at least one of thesecond adaptive filters.
 20. The audio enhancement system of claim 19,adaptation feedback provided by at least one of the second adaptivefilters being based on a non-linear function.
 21. An acousticreverberation reduction system, comprising: an adaptive filter thatfilters a linear prediction output based, at least in part, upon aplurality of adaptive coefficients, the adaptive filter modifying atleast one of the plurality of adaptive coefficients based, at least inpart, upon a feedback output, the adaptive filter providing an acousticreverberation reduced output; and, a feedback component that providesthe feedback output based, at least in part, upon a non-linear functionof the acoustic reverberation reduced output.
 22. The acousticreverberation system of claim 21, further comprising a linear predictionanalyzer that analyzes an input signal and provides the linearprediction residual output.
 23. The acoustic reverberation system ofclaim 22, further comprising a linear prediction synthesis filter thatfilters the acoustic reverberation output and provides a processedoutput signal.
 24. The acoustic reverberation reduction system of claim21, the non-linear function being based, at least in part, uponmaximization of kurtosis.
 25. The acoustic reverberation reductionsystem of claim 24, the non-linear function being based, at least inpart, upon the following equation: J(n)=E{{tilde over (y)} ⁴(n)}/E ²{{tilde over (y)} ²(n)}−3 where J(n) is the kurtosis metric, y(n) is theacoustic reverberation reduced output, and, and E{ } are expectations.26. The acoustic reverberation reduction system of claim 24, thenon-linear function being based, at least in part, upon selecting athreshold, and providing positive feedback for samples above thatthreshold and negative feedback for samples below that threshold. 27.The acoustic reverberation system of claim 21, the feedback componentemploying LPC filtering of the acoustic reverberation reduced output,the feedback output being based, at least in part, upon a non-linearfunction based on a LPC residual.
 28. The acoustic reverberation systemof claim 21, the feedback component employing LPC filtering of theacoustic reverberation reduced output, the feedback output being based,at least in part, upon a non-linear function based on a LPC residualwhich increases the impulse-train characteristics of the LPC residual.29. The acoustic reverberation system of claim 21, the feedbackcomponent employing LPC filtering of the acoustic reverberation reducedoutput, the feedback output being based, at least in part, upon anon-linear function based on a LPC residual which increases the kurtosisof the LPC residual.
 30. An acoustic reverberation reduction system,comprising: a first adaptive filter that filters an input signal based,at least in part, upon a plurality of adaptive coefficients, the firstadaptive filter providing an acoustic reverberation reduced output; asecond adaptive filter that filters a linear prediction output based, atleast in part, upon the plurality of adaptive coefficients, the secondadaptive filter modifying at least one of the plurality of adaptivecoefficients based, at least in part, upon a feedback output, the secondadaptive filter further providing an output; and, a feedback componentthat provides the feedback output based, at least in part, upon anon-linear function of the output of the second adaptive filter.
 31. Theacoustic reverberation reduction system of claim 30, further comprisinga linear prediction analyzer that analyzes the input signal and providesthe linear prediction residual output;
 32. The acoustic reverberationreduction system of claim 30, the non-linear function being based, atleast in part, upon maximization of kurtosis.
 33. The acousticreverberation reduction system of claim 32, the non-linear functionbeing based, at least in part, upon the following equation:J(n)=E{{tilde over (y)} ⁴(n)}/E ² {{tilde over (y)} ²(n)}−3 where J(n)is the kurtosis metric, y(n) is the acoustic reverberation reducedoutput, and, and E{ } are expectations.
 34. The acoustic reverberationreduction system of claim 30, the first adaptive filter furthercomprising a frequency transform component that performs a frequencydomain transform of the input signal, filtering of the first adaptivefilter being performed on the frequency transformed input signal, thefirst adaptive filter further comprising an inverse frequency transformcomponent that performs an inverse frequency domain transform of thefiltered frequency transformed input signal.
 35. The acousticreverberation reduction system of claim 34, the frequency domaintransform being a Modulated Complex Lapped Transform.
 36. The acousticreverberation reduction system of claim 34, the second adaptive filterfurther comprising a frequency transform component that performs afrequency domain transform of the input signal, filtering of the secondadaptive filter being performed on the frequency transformed inputsignal, the second adaptive filter further comprising an inversefrequency transform component that performs an inverse frequency domaintransform of the filtered frequency transformed input signal.
 37. Theacoustic reverberation reduction system of claim 36, the frequencydomain transform of the second adaptive filter being a Modulated ComplexLapped Transform.
 38. An acoustic reverberation reduction system,comprising: a plurality of reverberation reduction channel components,at least some of the reverberation reduction channel componentscomprising a first adaptive filter comprising a frequency transformcomponent that performs a frequency domain transform of an input signalof the channel, the first adaptive filter filtering the frequencytransformed input signal based, at least in part, upon a plurality ofadaptive coefficients, the first adaptive filter further comprising aninverse frequency transform component that performs an inverse frequencydomain transform of the filtered frequency transformed input signal andprovides an acoustic reverberation reduced output; a linear predictionanalyzer that analyzes the input signal of the channel and provides alinear prediction residual output; a second adaptive filter comprising afrequency transform component that performs a frequency domain transformof the linear prediction residual output, the second adaptive filterfiltering the linear prediction residual output based, at least in part,upon a plurality of adaptive coefficients, the second adaptive filterfurther comprising an inverse frequency transform component thatperforms an inverse frequency domain transform of the filtered frequencytransformed linear prediction residual output and provides an output;and a summing component that sums the plurality of outputs of the secondadaptive filters; and, a feedback component that modifies at least oneof the plurality of adaptive coefficients based, at least in part, upona non-linear function of the output of summing component.
 39. Theacoustic reverberation reduction system of claim 38, the non-linearfunction being based, at least in part, upon maximization of kurtosis.40. The acoustic reverberation reduction system of claim 38, thefrequency domain transform of at least one of the first adaptive filterand the second adaptive filter being a Modulated Complex LappedTransform.
 41. The acoustic reverberation reduction system of claim 38,data used to drive the second adaptive filter being stored in thesystem.
 42. The acoustic reverberation reduction system of claim 41, thedata used to drive the second adaptive filter not being synchronizedwith the input signal.
 43. The acoustic reverberation reduction systemof claim 38, the data used to drive the second adaptive filter beingbased, at least in part, upon the presence of voiced speech.
 44. Theacoustic reverberation reduction system of claim 38, a data rate of thefirst and second adaptive filters being different.
 45. The acousticreverberation reduction system of claim 38, the data on each channelcorresponding to a signal from different microphones.
 46. An acousticnoise reduction system, comprising: a first signal statistics componentthat stores statistics associated with a certain portion or type of aninput signal; a second signal statistics component that storesstatistics associated with a second portion or type of the input signal;and, a spatial filter that provides an output signal, the output signalbeing based, at least in part, upon a filtered input signal, thefiltering being based, at least in part, upon a weighted errorcalculation based on the two classes of statistics.
 47. The acousticnoise reduction system of claim 46, the first signal statisticscomponent being the statistics of noise-like segments of the inputsignal, and, the second signal statistics component being the statisticsof signal+noise segments of the input signal.
 48. The acoustic noisereduction system of claim 46, further comprising a voice activitydetector that facilitates storing of at least one of the firststatistics and the second statistics.
 49. The acoustic noise reductionsystem of claim 46, filtering being performed in the frequency domain.50. An acoustic noise reduction system, comprising: a filter thatfilters an input signal based, at least in part, upon a plurality ofadaptive coefficients, the first adaptive filter providing an acousticnoise reduced output; a signal+noise buffer that stores a signal+noiseportion of the input signal; a noise buffer that stores a noise portionof the input signal; a signal composer that generates a synthetic inputsignal and a synthetic desired signal based, at least in part, uponinformation stored in at least one of the signal+noise buffer and thenoise buffer a LMS filter that filters the synthetic input signal based,at least in part, upon the plurality of adaptive coefficients, thefiltering being based, at least in part, upon a weighted errorcalculation of noise statistics and signal+noise statistics, the LMSfilter modifying at least one of the plurality of adaptive coefficientsbased, at least in part, upon a feedback output, the LMS filter furtherproviding an output; and, a differential component that provides thefeedback output based, at least in part, upon a difference between theLMS filter output and the synthetic desired signal.
 51. The acousticnoise reduction system of claim 50, the filtering of at least one of thefirst adaptive filter and the second adaptive filter being done in thefrequency domain.
 52. An acoustic signal enhancement system, comprising:a voice activity noise detector that provides information to a noisebuffer and a signal+noise buffer; a filter that filters the input signalbased, at least in part, upon a plurality of adaptive coefficients, thefilter providing an acoustic enhanced signal output; a noise adaptivefilter that that filters information stored in the noise buffer based,at least in part, upon the plurality of adaptive coefficients, the noisereduction adaptive filter modifying at least one of the plurality ofadaptive coefficients based, at least in part, upon a noise reductionfeedback output, the noise reduction adaptive filter further providingan output; a noise feedback component that provides the noise reductionfeedback output based, at least in part, upon a weighted errorcalculation of the output of the noise reduction adaptive filter. areverberation adaptive filter that filters at least a part of the speechsignal based, at least in part, upon the plurality of adaptivecoefficients, the reverberation reduction adaptive filter modifying atleast one of the plurality of adaptive coefficients based, at least inpart, upon a reverberation feedback output, the reverberation reductionadaptive filter further providing an output; and, a reverberationfeedback component that provides the reverberation feedback outputbased, at least in part, upon a non-linear function of the output of thereverberation reduction adaptive filter.
 53. A method for reducingacoustic reverberation, comprising: providing an acoustic reverberationreduced output based, at least in part, upon a plurality of adaptivecoefficients; modifying at least one of the plurality of adaptivecoefficient based, at least in part, upon a feedback output; and,providing the feedback output based, at least in part, upon a non-linearfunction of the acoustic reverberation reduced output.
 54. The method ofclaim 53, the non-linear function being based, at least in part, uponmaximization of kurtosis.
 55. A method for reducing acousticreverberation, comprising: filtering an input signal based, at least inpart, upon a plurality of adaptive coefficients; providing an acousticreverberation reduced output based, at least in part, upon the filteredinput signal; providing a linear prediction residual output based on theinput signal; filtering the linear prediction residual output based, atleast in part, upon the plurality of adaptive coefficients; modifying atleast one of the plurality of adaptive coefficients based, at least inpart, upon a feedback output; and, providing the feedback output based,at least in part, upon a non-linear function of the filtered linearprediction residual output.
 56. The method of claim 55, the non-linearfunction being based, at least in part, upon maximization of kurtosis.57. A method for reducing acoustic noise, comprising: storing noisestatistics associated with a noise portion of an input signal; storingsignal+noise statistics with a signal and noise portion of the inputsignal; and, filtering the input signal based, at least in part, upon aweighted error calculation of the noise statistics and the signal+noisestatistics.
 58. A method for reducing acoustic noise, comprising:storing a noise portion of an input signal; storing a signal+noiseportion of the input signal; filtering the input signal based, at leastin part, upon a plurality of adaptive coefficients; generating asynthetic input signal and a synthetic desired signal based, at least inpart, upon at least one of the stored noise portion of the input signaland the stored signal+noise portion of the input signal; providing anLMS filter output based, at least in part, upon the plurality ofadaptive coefficients, the filtering being based, at least in part, upona weighted error calculation of noise statistics and signal+noisestatistics, modifying at least one of the plurality of adaptivecoefficients based, at least in part, upon a feedback output; and,providing the feedback output based, at least in part, upon a differencebetween the LMS filter output and the synthetic desired signal.
 59. Amethod for enhancing an acoustic signal, comprising: storing noiseinformation associated with a noise portion of an input signal; storingsignal+noise information with a signal and noise portion of the inputsignal; filtering the input signal based, at least in part, upon aplurality of adaptive coefficients; providing a noise adaptive filteroutput signal based, at least in part, upon the stored noise informationand the plurality of adaptive coefficients; modifying at least one ofthe plurality of adaptive coefficients based, at least in part, upon anoise feedback output; providing the noise feedback output based, atleast in part, upon a weighted error of the noise statistics and thesignal+noise statistics; providing a reverberation filter output based,at least in part, upon the stored signal+noise information and theplurality of adaptive coefficients; modifying at least one of theplurality of adaptive coefficients based, at least in part, upon areverberation feedback output; and, providing the reverberation feedbackoutput based, at least in part, upon a non-linear function of the outputof the reverberation adaptive filter.
 60. A data packet transmittedbetween two or more computer components that facilitates acousticreverberation reduction, the data packet comprising: a data fieldcomprising a plurality of adaptive coefficients, at least one of theplurality of adaptive coefficients having been modified based, at leastin part, upon a feedback output based, at least in part, upon anon-linear function of an acoustic reverberation reduced output.
 61. Adata packet transmitted between two or more computer components thatfacilitates acoustic noise reduction, the data packet comprising: a datafield comprising a plurality of adaptive coefficients, at least one ofthe plurality of adaptive coefficients having been modified based, atleast in part, upon a feedback output based, at least in part, upon aweighted error calculation of noise statistics and signal+noisestatistics.
 62. A computer readable medium storing computer executablecomponents of a system facilitating acoustic reverberation reduction,comprising: an adaptive filter component that filters a linearprediction output based, at least in part, upon a plurality of adaptivecoefficients, the adaptive filter component modifying at least one ofthe plurality of adaptive coefficients based, at least in part, upon afeedback output, the adaptive filter component providing an acousticreverberation reduced output; and, a feedback component that providesthe feedback output based, at least in part, upon a non-linear functionof the acoustic reverberation reduced output.
 63. A computer readablemedium storing computer executable components of a system facilitatingacoustic noise reduction, comprising: a noise statistics component thatstores noise statistics associated with a noise portion of an inputsignal; a signal+noise statistics component that stores signal+noisestatistics associated with a signal and noise portion of the inputsignal; and, a spatial filter component that provides an output signal,the output signal being based, at least in part, upon a filtered inputsignal, the filtering being based, at least in part, upon a weightederror calculation of the noise statistics and the signal+noisestatistics.
 64. An acoustic reverberation reduction system, comprising:means for providing an acoustic reverberation reduced output based, atleast in part, upon a plurality of adaptive coefficients; means formodifying at least one of the plurality of adaptive coefficient based,at least in part, upon a feedback output; and, means for providing thefeedback output based, at least in part, upon a non-linear function ofthe acoustic reverberation reduced output.
 65. An acoustic noisereduction system, comprising: means for storing noise statisticsassociated with a noise portion of an input signal; means for storingsignal+noise statistics with a signal and noise portion of the inputsignal; and, means for filtering the input signal based, at least inpart, upon a weighted error calculation of the noise statistics and thesignal+noise statistics.